code: def cond_fake_tensor_mode(mode, pred, true_fn, false_fn, operands):
      with mode:
          true_outs = true_fn(*operands)
          flat_true_outs = pytree.tree_leaves(true_outs)
          flat_false_outs = pytree.tree_leaves(false_fn(*operands))
      if len(flat_true_outs) != len(flat_false_outs):
          raise RuntimeError(
              "Unmatched number of outputs from cond() branches."
          )
  
      for true_out, false_out in zip(flat_true_outs, flat_false_outs):
          true_meta = _extract_tensor_metadata(true_out)
          false_meta = _extract_tensor_metadata(false_out)
          if true_meta != false_meta:
              raise torch._dynamo.exc.CondOpArgsMismatchError(
                  f"Expected each tensor to have same metadata but got:"
                  f"\n  {true_fn.__name__} returns {true_meta}"
                  f"\n  {false_fn.__name__} returns {false_meta}"
              )
      return true_outs
  
imports: from contextlib import contextmanager
  from dataclasses import dataclass
  import torch
  import torch._subclasses.functional_tensor
  import torch.fx.traceback
  import torch.utils._pytree
  from torch._C import DispatchKey
  from torch._C._functorch import _add_batch_dim, get_unwrapped, is_batchedtensor, maybe_get_bdim
  from torch._functorch.utils import exposed_in
  from torch._higher_order_ops.utils import autograd_not_implemented
  from torch._ops import HigherOrderOperator
  from torch._subclasses.fake_tensor import FakeTensorMode
  from torch.fx.experimental.proxy_tensor import disable_proxy_modes_tracing, make_fx, ProxyTorchDispatchMode, track_tensor_tree
  from torch.fx.passes.shape_prop import _extract_tensor_metadata
  from torch.multiprocessing.reductions import StorageWeakRef
  from torch.utils._python_dispatch import _get_current_dispatch_mode
label_desc: Use image node_id 7 for calling a global function with example usage: cond_fake_tensor_mode(mode, pred, true_fn, false_fn, operands) and returns: true_outs